Smartphones and portable devices have been widely used in our daily life. However, these portable devices cannot be used in a lot of environments due to limitations in battery capacity and computing speed. Therefore, how to save energy consumption and improve processing ability has become a hot issue. The previous works were mainly to transfer some local tasks to remote compute nodes by intelligently selecting the execution route of tasks based on Directed Acyclic Graph (DAG). Different from previous work, this work mainly attempts to consider task urgency and system load based on Dynamic Voltage and Frequency Scaling (DVFS) model. Firstly, tasks are given different priorities according to the energy consumption of different locations and task urgency. Secondly, a reference working state is given according to system load to ensure that each task (regardless of job deadline) is completed with minimum energy consumption under the average system load. Finally, a heuristic algorithm is used to schedule tasks on mobile devices and remote cloud resources based on the priority of tasks and reference status of resources. Our method is called AODVFS-Adaptive Offloading based on DVFS model. Comparison with other methods shows that our method saves average energy consumption and increases the number of completed tasks.

智能手机和便携式设备已广泛应用于我们的日常生活中。但是，由于电池容量和计算速度的限制，这些便携式设备不能在很多环境中使用。因此，如何节省能源消耗和提高加工能力已成为热门问题。以前的工作主要是通过基于有向无环图（DAG）智能选择任务的执行路径，将一些本地任务转移到远程计算节点。与以前的工作不同，这项工作主要尝试基于动态电压和频率缩放（DVFS）模型来考虑任务紧急性和系统负载。首先，根据不同地点的能源消耗和任务紧迫性，为任务分配不同的优先级。第二，根据系统负载给出参考工作状态，以确保在平均系统负载下以最少的能耗完成每个任务（无论工作期限如何）。最后，基于任务的优先级和资源的参考状态，使用启发式算法在移动设备和远程云资源上调度任务。我们的方法称为基于DVFS模型的AODVFS自适应卸载。与其他方法的比较表明，我们的方法节省了平均能耗，并增加了已完成任务的数量。我们的方法称为基于DVFS模型的AODVFS自适应卸载。与其他方法的比较表明，我们的方法节省了平均能耗，并增加了已完成任务的数量。我们的方法称为基于DVFS模型的AODVFS自适应卸载。与其他方法的比较表明，我们的方法节省了平均能耗，并增加了已完成任务的数量。